Peterson (2003)

Peterson (2003) online here is an influential study cited by IPCC AR4 purporting to show that the urbanization effect is negligible. It concluded:

Using satellite night-lights—derived urban/rural metadata, urban and rural temperatures from 289 stations in 40 clusters were compared using data from 1989 to 1991. Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures.

AR4 said of this study:

Over the conterminous USA, after adjustment for time-of-observation bias and other changes, rural station trends were almost indistinguishable from series including urban sites (Peterson, 2003; Figure 3.3… One possible reason for the patchiness of UHIs is the location of observing stations in parks where urban influences are reduced (Peterson, 2003).

The 289 stations are not listed in the article and no SI is available. One of our readers inquired about the stations and I wrote to Peterson asking him for the information as follows:

Dear Dr Peterson, could you please provide me a list of the USHCN id numbers of the 289 stations used in Peterson 2003, together with information on how they are allocated to the 40 clusters and how they are classified rural-urban? It would be helpful if you provided an SI with this sort of information. Thanks, Steve McIntyre

As I anticipated, Peterson responded promptly with the list together which I’ve posted up here with some additional information that I’ve collated. The first 5 columns are from Peterson. Peterson did not provide lat-long’s; I looked these up in the state files at http://www.wrcc.dri.edu/inventory/sodca.html (CA) etc. I also compared the ID numbers to USHCN id numbers and marked the USHCN-overlapping series. Peterson added the following additional comments:

a) If you read the article, you will notice that I did not specifically use USHCN stations in the analysis.

b) If you read the article, you will notice that I did not classify the stations as rural or urban. That was done by Owen et al. (1998) based on night lights data and used by Gallo and Owen (1999). Kevin Gallo provided me with the metadata he used in Gallo and Owen. I’m sure Dr. Gallo would be happy to answer any questions you might have that are not already addressed in those two papers.

c) The attached file is a list of the 289 stations I evaluated and processed for Peterson 2003. The first number is the group, 1-40; the second column is the rural/urban satellite nigh lights based metadata classification; the third column is the station number and the fourth the station name. If you read Peterson and Owen (2005) you will come across this statement: “The mean urban minus rural difference is 0.03°C using adjusted data and 0.00 with the modified adjusted data. The first result differs slightly from the 0.04°C reported in Peterson (2003) with the difference due to correcting a processing error in the metadata assignments at a few of the stations.” The metadata I provided you are the corrected metadata.

Of the 289 stations, only 63 came from the USHCN network.

In the article, Peterson said that 85 of these stations were rural, 191 urban, and 13 suburban. In the file, there were 84 rural, 6 suburban and 199 urban. Although Peterson attributed the 40 clusters to Gallo and Owen 1999, that article only used 28 clusters and not all of the 28 clusters could be identified in the Peterson list. So some additional selection procedure has been applied.

I did some cross-analyses of the 63 USHCN stations in the Peterson network – the USHCN network being said elsewhere to be mostly “rural”. Of these stations, 13 were rural, 2 suburban and 48 urban.

On an earlier occasion, I did a concordance of USHCN identification numbers to GISS lights – such concordances take a surprising amont of time and I used this information to cross-check GISS lights against Peterson’s network – with the usual surprising results. Of the 63 USHCN stations in the Peterson network, 9 had GISS-lights of 0. However, 3 of the 9 sites with lights=0 were classed by Peterson as urban (Fort Yates ND; Utah Lake Lehi UT; Fort Valley AZ) while 6 were classified as rural.

Of the 13 Peterson USHCN sites classified as “rural”, the GISS lights were as high as 19. Checking the 48 Peterson USHCN sites classified as “urban”, 15 had GISS lights less than or equal to 19 (including 3 with lights=0 as noted above.)

One of the Peterson clusters is in California, where surfacestations.org has a strong survey presence. Here Peterson compared 7 sites classified as “urban”: USHCN sites DAVIS 2 WSW EXP FARM, Lodi; non-USHCN urban sites: Placerville, Sacramento FAA AP, Sacramento WSO City, Antioch Pump Plant, Folson Dam; against one rural non-USHCN non-GHCN site: Camp Pardee. I’m not sure what exactly this proves. If the USHCN is supposed to be a “high quality” network, it’s puzzling that so many sites in the Peterson network do not come from either this network or the GHCN network.

In a follow-up email, Peterson said that the data could be obtained from http://www.ncdc.noaa.gov/oa/mpp/digitalfiles.html which unfortunately is a pay-for-view site. JerryB observed that most of the identification numbers could be matched in the GHCND station inventory. I tested this and matched all but 3 sites (Kissimmee, New Orleans Audubon and Red Rock NV) to GHCND identifications. With a change of one digit, New Orleans Audubon and Kissimmee can be matched to GHCND series. The GHCND station inventory seems to missing NV stations from the last half of the alphabet. Daily data for each of these stations can be downloaded from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/all by adding a prefix and suffix to the station id. For example:

I haven’t done any further tests on this yet, but spot checking of some clusters seems quite practical. Though the basis for selection of these comparisons seems very unclear. See related posts here and here.)

SteveM. Your above analysis seems to corroborate your previous temperature site analysis indicating a significant tendency to overlap the urban and rural data in the final calculations. That also further supports my belief that Anthony Watts and surfacestations.org may lead to further, valuable insights and is definitely worth the effort. However, it is still early in the process, and I note you have been unable to aquire much of the data and analysis information you need to confirm any such overlap.

If this is the correct list I’ve been to 3 of the sites in South Carolina. Winnsboro is posted on Surface Stations and is a City Water Plant. Little Mountain is a private residence. Parr is a power station, it was located at a nuclear power station, but is now at a coal fired station. The University of South Carolina site is on a urban campus. Camden 3W is no longer in service.

Further to #3: following Jerry’s identification of station id matches in the GHCND file, I was able to identify an online source of daily data for all but 3 Peterson stations. It will take some time to make monthly collations and experiment a little. IT’s hard to figure out why these particular stations were selected out of the entire population.

We hear lots of criticism about supposed non-randomness of surfacestionas.org – which is however trying for a census, not random sampling. But we never hear much complaint about the seemingly arbitrary selection procedures of articles like this.

I just want to commend Dr. Peterson for providing the information to Steve upon request. This is how science is supposed to work. Now don’t get me wrong, I still have problems with his study and it’s conclusions, but it’s nice to see a responsive climate scientist.

As I have wondered so many times before, how many “rural” stations have incurred an imposed positive temperature contribution due to microsite issues and the generally increasing trends in energy usage and land surface modification, even in rural areas? UHI isn’t limited to U ….

A curious legacy of open country standardization is that many urban stations are
placed over short grass in open locations (parks, playing fields). As a result they monitor
modified rural-type conditions, not representative urban ones (Peterson, 2003).

Is this an urban legend? or did peterson actually survey the sites, measure the grass length, etc etc.

Now, Oke’s paper makes a nice companion piece for Parker ( explaining boundary layers and such)
But I have to wonder how many people in addition to Parker and Oke accepted this “urban legend” of
sites being located in parks.

Should be instructive the get a final list and have a look. Or perhaps Peterson has Photos?

I have done some comparisons of rural versus urban sites and found a distinct difference for many parts of the world. See the effects of urbanization section in Part 3 at http://www.appinsys.com/GlobalWarming. See also the regional summaries on the same web site.

Mineral is non existent as a town. In the middle of nowhere northern California.
1 gas station. No stores. No hotels. a few dozen houses, Nothing there.last time
through there I didnt see the site, I can’t say much. If its at the ranger station,
then its back in the woods.

Orland is down the road and around the corner. Mineral is rural but its
1/3 the way up the foothills of the damn volcano. elevation is like 4K feet.

Briffa has some tree rings ( mountain hemlock)
taken from this area. Its remote. Volcano erupted there back in 1915.

More weirdness from PEterson 2003: 6 of the 40 “clusters” contain only “urban” stations, no “rural” comparanda; one of the clusters contains only “suburban” stations; two of the clusters in the list contain no stations.

The cluster with all “s” stations includes Orland together with De Sabla, Mineral, Red Bluff FSS, Paradise, Redding WSO.

HArd to understand why he creates clusters without both urban and rural candidates.

Looking at the South Carolina sites, I can’t understand why Wedgefield could be considered an urban site. I have not been to Wedgefield (yet its not a USHCN site) but I can not believe its any more urban than Little Mountain which is considered rural.

The Peterson selected database of 289 stations looks like a cherry orchard. Of the 48 contiguous States, 39 are included in Peterson’s DB, leaving 9 that aren’t. These nine are: DE, GA, IN, MD, MT, NH, NY, OK, VT, WV. Consider that there are no stations from NY, GA, IN, MD, OK! These are not small areas like DE or RI.

The characterization of sites as Urban, Suburban or Rural appears to be frivolous and chimerical. A site’s characteristics are more closely correlated with conditions within 100 feet of the instrumentation than whether it is part of an urban, suburban or rural area as defined by night lights…puhleeze! Proximity to major urban areas is important, but should be evaluated on some basis such as energy consumption of the defined urban area as a proxy for heat production.

The data is made slightly more difficult to analyze by the difference in group designations between USHCN and the Peterson data. Both groups are ordered alphabetically, but the Peterson data are alphabetized by State Zip-code designations while the USHCN data (and ID numbers) are by full State names. Thus Nevada according to USHCN is group #26 (NEvada), while in Peterson’s data it is under group code #31 (NV).

The selection of data comprising the 289 stations used in Peterson’s base does not appear to have been random. Thus, statistical analysis that is based on random sampling is invalid. A good characterization of this, and one often misgauged by statisticians, is the so-called Monty Hall problem named for the selection of doors by contestants on “Let’s Make a Deal”.

Even though it probably should have been described and explained in the paper itself (although curiously that is non-standard to do so, so not unexpected or out of the ordinary) I also commend Dr. Peterson for supplying the data. So far however, it appears that the selection of the sites is not exactly as was made out to be. Is that an accurate statement, or am I mis-interpreting what is being said here? Anyone care to comment on the degree to which that opinion is correct or incorrect? I hate to say it, but in my opinion, it looks like this is an example of why data is not usally shared.

There is now a large amount of personal weather station data available on the internet. These are home, school, port, business, etc weather stations, of reasonably good quality, which post their data on the web.

So, out of curiosity, I checked the Houston TX US stations for last night (May 4, 2008) as it was a clear night with relatively low humidity and a light wind. My goal was to see if an UHI fingerprint could be seen in the data.

The raw numbers are on this map and are repeated, with hand-drawn contours below:

Looks to me like a fingerprint, with a maximum effect of maybe 5 to 7F. It is displaced somewhat to the southwest of midcity, probably due to the northeast wind.

Re #32 Steve, the stations in urban Houston used by GISS are listed here . One is active, one is semi-active (hasn’t been updated since 2004) and the other two are inactive but were used in the historical analysis.
The nearby USHCN stations are the yellow pins on this image .

A combined map showing the Houston UHI and nearby “official” stations is here . It turned out too dark so I will transfer this to a white background. The maximum UHI appears to be 5 to 8 degrees F, displaced to the southwest of the city center by the northeasterly winds.

Site A and perhaps site B are the two active GISS sites. The USHCN sites are E and F.

Normal winds are from the southeast, which would displace the UHI towards the north and west.

David – interesting idea. If you got the data from Weather Underground, I believe they also have lat – long coordinates for the personal weather stations. You could use this info with plotting software such as Surfer to generate the contours as an overlay for the map. It would be more accurate and look cleaner. It is very difficult to draw accurate contours by hand.

If you don’t have Surfer, I would be happy to plot the contours for you if you include the lat-long info in the data file.

This really is a fascinating idea. The amount of real-time data available online from personal weather stations could allow the UHI effect to be documented at levels never seen before. I would think there are a number of US cities that would make ideal candidates that would not require data manipulation to account for topography, elevation, or influences from adjacent bodies of water. Some examples might be Indianapolis, Oklahoma City, Fresno, Dallas or Tuscon. All are out in the middle of nowhere on relatively flat terrain.

If the data were collected over a prolonged period, animations of how the contours change from day/night, as seasons change, as wind speed/direction change, etc. could be compliled. The implications could be huge.

Re #39 jc, those of us in the Houston area, like Leif, would probably welcome some elevation change. I’m afraid that midcity is about 50 feet ASL while fifty miles to the northeast the elevation only approaches 100 feet ASL. Not much elevation change in this region.

Re #40 Jeff, I agree about the potential value of the huge amount of personal weather station data. I’m optimistic that, with caution, we may be able to learn some things.

There may be a guest post at Anthony Watts site regarding personal weather station “clusters” (personal stations in close proximity to one another which likely differ only in microsite factors like nearby trees and concrete). An example of the temperatures in such a cluster is here , where the temperatures stay close to one another until microsite effects kick in during daylight.

On a cloudy night those lines stay quite close to one another. On a clear, low-humidity night (not shown) they spread apart, again apparently due to microsite effects.

Re #41 David Smith – In hindsight, it was a mistake for me to say that the changes in elevation are “significant”, but they are somewhat greater than your description. Looking at http://nmviewogc.cr.usgs.gov/viewer.htm, I find for example that Conroe has an elevation of 166 feet, compared to central Houston’s elevation of about 50 feet. However, the observed temperature difference between Conroe and central Houston was about ten degrees, which clearly is far too large a change to be accounted for solely by the relatively small change in elevation.

The point I was trying to muddle towards was expressed very well by Jeff C – rather than looking at Houston, it would be better to look at cities with minimal influence from topography, elevation, or influences from adjacent bodies of water.

[Side remark to Jeff C: There are 7000 foot peaks within 20 miles of Tucson, so it would appear not to be a particularly good candidate. Indianapolis and Oklahoma City, however, seem to be first-rate.]

jc, I agree – Houston has a lot of drawbacks. For me it has the advantage of being my backyard while I try to develop an approach. What do you think about Kansas City or Tulsa as additional possibilities?

David Smith…I would like too see road temp readings from roads
with as little traffic as possible. How does Texas road administration
function?? Best Staffan (Still alive in spite of 16C drop in
temperatures in 20 HOURS…Sweden is NOT South Dakota you know…!)

Here’s the December 11, 2008 UHI map for Houston, at dawn. The green dot is the official weather station, which I estimate experienced a UHI effect of 2 or 3F. The maximum UHI effect in the metropolitan area appeared to be 6 to 8 F.